爆炸排淤填石法中淤泥的本构模型

以爆炸排淤填石法为背景 ,对不同应变率阶段淤泥的本构模型进行了分析 ,利用ANSYS/LS-DYNA动态有限元分析程序对所选择的模型进行验证和确认。计算和分析结果表明 :在形成爆炸空腔的高应变率阶段 ,淤泥表现为理想不可压缩流体的性质 ;在小药量小抵抗线条件下 ,甚至在淤泥自重作用下的较低应变率变形阶段 ,其黏性效应也可以忽略不计。     

A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

A large-scale dynamically weighted directed network (DWDN) involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications, like in a terminal interaction pattern analysis system (TIPAS). It can be represented by a high-dimensional and incomplete (HDI) tensor whose entries are mostly unknown. Yet such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN. A latent factorization-of-tensors (LFT) model proves to be highly efficient in extracting such knowledge from an HDI tensor, which is commonly achieved via a stochastic gradient descent (SGD) solver. However, an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale DWDNs. To address this issue, this work proposes a proportional-integral-derivative (PID)-incorporated LFT model. It constructs an adjusted instance error based on the PID control principle, and then substitutes it into an SGD solver to improve the convergence rate. Empirical studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models, the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.      

基于张量分解融合RGB-D图像的物体识别

为了充分利用RGB-D图像的深度图像信息，提出了基于张量分解的物体识别方法。首先将RGB-D图像构造成一个四阶张量，然后将该四阶张量分解为一个核心张量和四个因子矩阵，再利用相应的因子矩阵将原张量进行投影，获得融合后的RGB-D数据，最后输入到卷积神经网络中进行识别。RGB-D数据集中三组相似物体的识别结果表明，利用张量分解融合RGB-D图像的物体识别准确率高于未采用张量分解的物体识别准确率，并且单一错分实例的准确率最高可提升99%。     

Shape from focus based on 3D structure tensor using optical microscopy

Shape from focus (SFF) is a widely used passive optical method for 3D shape reconstruction. In SFF, a focus measure, which is used to estimate the relative focus level, plays a critical role in depth estimation. In this article, we present a new focus measure for accurate 3D shape estimation in optical microscopy based on the analysis of 3D structure tensor. First, the 3D tensors are computed from the input image sequence for each pixel. Then, each tensor is decomposed into point, curve, and surface tensors by decomposing tensors into eigenvalues and eigenvectors. Finally, the surfaceness is used to measure the quality of sharpness. The proposed focus measure provides accurate focus values and better resistance against noise. The proposed measure is evaluated by conducting experiments using image sequences of simulated and microscopic real objects. The comparative analysis demonstrates the effectiveness of the proposed focus measure in recovering 3D shape.     

Analytical study of surface wave multiple refraction in boundary of a scalar impedance surface with a tensor impedance surface

In this paper, the multiple refraction phenomenon is investigated on the boundary of a scalar impedance surface (SIS) and a tensor impedance surface (TIS). When a surface wave (SW) propagates on the SIS and radiates to the boundary of the TIS, the propagation direction of it is changed and the refraction phenomenon is accrued. The method that is proposed in this paper can predict the multiple refraction for the SW. Moreover, another analytical method is introduced for designing the proposed structure which the double refraction (DR) occurs at arbitrary angles on it. Using it, a sample of the structure is designed by printed circuits in 15.2GHz and the results are verified by the full‐wave simulation and measurement. The results are shown that in the structure, DR is occurred in 2° and 22° as predicted. The proposed method can provide many applications such as design of SW power dividers based on the TISs, impedance surface based waveguides, holographic antennas, and feeding of array antennas.     

Spin reorientation,normal and inverse magnetocaloric effects in heavy rare-earth iron garnets

Experiments for heavy rare-earth iron garnets Ho₃Fe₅O₁₂ and Er₃Fe₅O₁₂ show a compensation effect characterized by a near zero magnetization at 134 K and 80 K, respectively. The magnetic entropy change calculated according to the Maxwell's relation is shown to be positive and negative, indicating both normal and inverse magnetocaloric effects exist in the sample. The critical temperature (134 K for Ho₃Fe₅O₁₂, 80 K for Er₃Fe₅O₁₂) of the two types of magnetocaloric effect occurs at almost the same temperature as the compensation point. However, the compensation effect is attributed to the multiple exchange interactions among the octahedral sites Fe³⁺, the tetrahedral sites Fe³⁺ and the dodecahedral sites R³⁺, while the reversal of the magnetocaloric effect is originated from the spin reorientation. The maximum magnetic entropy change of the normal magnetocaloric effect is 4.72 J kg⁻¹ K⁻¹ for Ho₃Fe₅O₁₂ at 34 K and 4.94 J kg⁻¹ K⁻¹ for Er₃Fe₅O₁₂ at 24 K, respectively. Moreover, all the positive slopes of basic Arrott plots suggest only the second-order phase transition existing in Ho₃Fe₅O₁₂ and Er₃Fe₅O₁₂.     

Images of a First‐Order Spin‐Reorientation Phase Transition in a Metallic Kagome Ferromagnet

First‐order phase transitions, where one phase replaces another by virtue of a simple crossing of free energies, are best known between solids, liquids, and vapors, but they also occur in a wide range of other contexts, including even elemental magnets. The key challenges are to establish whether a phase transition is indeed first order, and then to determine how the new phase emerges because this will determine thermodynamic and electronic properties. Here it is shown that both challenges are met for the spin reorientation transition in the topological metallic ferromagnet Fe₃Sn₂. The magnetometry and variable temperature magnetic force microscopy experiments reveal that, analogous to the liquid–gas transition in the temperature–pressure plane, this transition is centered on a first‐order line terminating in a critical end point in the field‐temperature plane. The nucleation and growth associated with the transition is directly imaged, indicating that the new phase emerges at the most convoluted magnetic domain walls for the high temperature phase and then moves to self‐organize at the domain centers of the high temperature phase. The dense domain patterns and phase coexistence imply a complex inhomogenous electronic structure, which can yield anomalous contributions to the electrical conductivity.     

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.     

The phase structure,chain diffusion motion and local reorientation motion: C Solid-state NMR study on the highly-crystalline solid polymer electrolytes

In our previous paper, we investigated the influence of the phase structure on the polymeric chain diffusion in the complexed crystals of solid polymer electrolytes of PEO:LiAsF₆ and PEO:LiCF₃SO₃. We observed that with the increase of the crystallinity, the chain diffusion rate decreases dramatically. In this work, by employing the ¹³C CODEX NMR spectroscopy, we demonstrate that opposite to the behavior of the chain diffusion motion, the local reorientation motion of polymeric chains within the complexed crystals are greatly increased with the increase of the crystallinity, which is accompanied by the change of the phase structure. The relationship between the different molecular motions within the complexed crystals and the phase structure are discussed therefore.